Overview

Dataset statistics

Number of variables23
Number of observations1627
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory369.6 KiB
Average record size in memory232.6 B

Variable types

Numeric16
Categorical7

Alerts

Attrition_Flag has constant value ""Constant
Attrition_Num has constant value ""Constant
Card_Category is highly imbalanced (79.2%)Imbalance
CLIENTNUM has unique valuesUnique
Dependent_count has 135 (8.3%) zerosZeros
Total_Revolving_Bal has 893 (54.9%) zerosZeros
Avg_Utilization_Ratio has 893 (54.9%) zerosZeros

Reproduction

Analysis started2023-12-27 12:22:04.332426
Analysis finished2023-12-27 12:22:15.216635
Duration10.88 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

CLIENTNUM
Real number (ℝ)

UNIQUE 

Distinct1627
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3526138 × 108
Minimum7.0808328 × 108
Maximum8.2829493 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-12-27T07:22:15.258168image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum7.0808328 × 108
5-th percentile7.0886011 × 108
Q17.1237126 × 108
median7.1656443 × 108
Q37.6837368 × 108
95-th percentile8.1244094 × 108
Maximum8.2829493 × 108
Range1.2021165 × 108
Interquartile range (IQR)56002425

Descriptive statistics

Standard deviation35577253
Coefficient of variation (CV)0.048387219
Kurtosis-0.0057610876
Mean7.3526138 × 108
Median Absolute Deviation (MAD)4737450
Skewness1.2552264
Sum1.1962703 × 1012
Variance1.265741 × 1015
MonotonicityNot monotonic
2023-12-27T07:22:15.311712image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
708508758 1
 
0.1%
713597658 1
 
0.1%
714164808 1
 
0.1%
712593933 1
 
0.1%
712795833 1
 
0.1%
716099958 1
 
0.1%
714408933 1
 
0.1%
711605208 1
 
0.1%
719740008 1
 
0.1%
708519108 1
 
0.1%
Other values (1617) 1617
99.4%
ValueCountFrequency (%)
708083283 1
0.1%
708084558 1
0.1%
708108333 1
0.1%
708117933 1
0.1%
708121908 1
0.1%
708139833 1
0.1%
708152358 1
0.1%
708154833 1
0.1%
708158133 1
0.1%
708170508 1
0.1%
ValueCountFrequency (%)
828294933 1
0.1%
828291858 1
0.1%
828215508 1
0.1%
827984658 1
0.1%
827964858 1
0.1%
827898033 1
0.1%
827476983 1
0.1%
827451333 1
0.1%
827440458 1
0.1%
827117808 1
0.1%

Attrition_Flag
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
Attrited Customer
1627 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters27659
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAttrited Customer
2nd rowAttrited Customer
3rd rowAttrited Customer
4th rowAttrited Customer
5th rowAttrited Customer

Common Values

ValueCountFrequency (%)
Attrited Customer 1627
100.0%

Length

2023-12-27T07:22:15.360100image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-27T07:22:15.395857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
attrited 1627
50.0%
customer 1627
50.0%

Most occurring characters

ValueCountFrequency (%)
t 6508
23.5%
r 3254
11.8%
e 3254
11.8%
A 1627
 
5.9%
i 1627
 
5.9%
d 1627
 
5.9%
1627
 
5.9%
C 1627
 
5.9%
u 1627
 
5.9%
s 1627
 
5.9%
Other values (2) 3254
11.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22778
82.4%
Uppercase Letter 3254
 
11.8%
Space Separator 1627
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 6508
28.6%
r 3254
14.3%
e 3254
14.3%
i 1627
 
7.1%
d 1627
 
7.1%
u 1627
 
7.1%
s 1627
 
7.1%
o 1627
 
7.1%
m 1627
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
A 1627
50.0%
C 1627
50.0%
Space Separator
ValueCountFrequency (%)
1627
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26032
94.1%
Common 1627
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 6508
25.0%
r 3254
12.5%
e 3254
12.5%
A 1627
 
6.2%
i 1627
 
6.2%
d 1627
 
6.2%
C 1627
 
6.2%
u 1627
 
6.2%
s 1627
 
6.2%
o 1627
 
6.2%
Common
ValueCountFrequency (%)
1627
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 6508
23.5%
r 3254
11.8%
e 3254
11.8%
A 1627
 
5.9%
i 1627
 
5.9%
d 1627
 
5.9%
1627
 
5.9%
C 1627
 
5.9%
u 1627
 
5.9%
s 1627
 
5.9%
Other values (2) 3254
11.8%

Customer_Age
Real number (ℝ)

Distinct42
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.659496
Minimum26
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-12-27T07:22:15.437683image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile33.3
Q141
median47
Q352
95-th percentile59
Maximum68
Range42
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.6656516
Coefficient of variation (CV)0.16428921
Kurtosis-0.31267719
Mean46.659496
Median Absolute Deviation (MAD)5
Skewness-0.039750792
Sum75915
Variance58.762215
MonotonicityNot monotonic
2023-12-27T07:22:15.484848image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
48 85
 
5.2%
43 85
 
5.2%
44 84
 
5.2%
46 82
 
5.0%
45 79
 
4.9%
49 79
 
4.9%
41 76
 
4.7%
47 76
 
4.7%
50 71
 
4.4%
54 69
 
4.2%
Other values (32) 841
51.7%
ValueCountFrequency (%)
26 6
 
0.4%
27 3
 
0.2%
28 1
 
0.1%
29 7
 
0.4%
30 15
0.9%
31 13
0.8%
32 17
1.0%
33 20
1.2%
34 19
1.2%
35 21
1.3%
ValueCountFrequency (%)
68 1
 
0.1%
66 1
 
0.1%
65 9
 
0.6%
64 5
 
0.3%
63 8
 
0.5%
62 17
1.0%
61 17
1.0%
60 13
 
0.8%
59 40
2.5%
58 24
1.5%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
F
930 
M
697 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1627
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
F 930
57.2%
M 697
42.8%

Length

2023-12-27T07:22:15.529555image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-27T07:22:15.566206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
f 930
57.2%
m 697
42.8%

Most occurring characters

ValueCountFrequency (%)
F 930
57.2%
M 697
42.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1627
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 930
57.2%
M 697
42.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 1627
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 930
57.2%
M 697
42.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1627
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 930
57.2%
M 697
42.8%

Dependent_count
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4025814
Minimum0
Maximum5
Zeros135
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-12-27T07:22:15.600967image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2750103
Coefficient of variation (CV)0.53068351
Kurtosis-0.61894433
Mean2.4025814
Median Absolute Deviation (MAD)1
Skewness-0.10623519
Sum3909
Variance1.6256514
MonotonicityNot monotonic
2023-12-27T07:22:15.638452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 482
29.6%
2 417
25.6%
1 269
16.5%
4 260
16.0%
0 135
 
8.3%
5 64
 
3.9%
ValueCountFrequency (%)
0 135
 
8.3%
1 269
16.5%
2 417
25.6%
3 482
29.6%
4 260
16.0%
5 64
 
3.9%
ValueCountFrequency (%)
5 64
 
3.9%
4 260
16.0%
3 482
29.6%
2 417
25.6%
1 269
16.5%
0 135
 
8.3%

Education_Level
Categorical

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
Graduate
487 
High School
306 
Unknown
256 
Uneducated
237 
College
154 
Other values (2)
187 

Length

Max length13
Median length11
Mean length8.9446835
Min length7

Characters and Unicode

Total characters14553
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduate
2nd rowDoctorate
3rd rowGraduate
4th rowGraduate
5th rowGraduate

Common Values

ValueCountFrequency (%)
Graduate 487
29.9%
High School 306
18.8%
Unknown 256
15.7%
Uneducated 237
14.6%
College 154
 
9.5%
Doctorate 95
 
5.8%
Post-Graduate 92
 
5.7%

Length

2023-12-27T07:22:15.683405image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-27T07:22:15.728848image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
graduate 487
25.2%
high 306
15.8%
school 306
15.8%
unknown 256
13.2%
uneducated 237
12.3%
college 154
 
8.0%
doctorate 95
 
4.9%
post-graduate 92
 
4.8%

Most occurring characters

ValueCountFrequency (%)
a 1490
 
10.2%
e 1456
 
10.0%
o 1304
 
9.0%
t 1098
 
7.5%
d 1053
 
7.2%
n 1005
 
6.9%
u 816
 
5.6%
r 674
 
4.6%
c 638
 
4.4%
l 614
 
4.2%
Other values (15) 4405
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12130
83.4%
Uppercase Letter 2025
 
13.9%
Space Separator 306
 
2.1%
Dash Punctuation 92
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1490
12.3%
e 1456
12.0%
o 1304
10.8%
t 1098
9.1%
d 1053
8.7%
n 1005
8.3%
u 816
6.7%
r 674
 
5.6%
c 638
 
5.3%
l 614
 
5.1%
Other values (6) 1982
16.3%
Uppercase Letter
ValueCountFrequency (%)
G 579
28.6%
U 493
24.3%
S 306
15.1%
H 306
15.1%
C 154
 
7.6%
D 95
 
4.7%
P 92
 
4.5%
Space Separator
ValueCountFrequency (%)
306
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 92
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14155
97.3%
Common 398
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1490
 
10.5%
e 1456
 
10.3%
o 1304
 
9.2%
t 1098
 
7.8%
d 1053
 
7.4%
n 1005
 
7.1%
u 816
 
5.8%
r 674
 
4.8%
c 638
 
4.5%
l 614
 
4.3%
Other values (13) 4007
28.3%
Common
ValueCountFrequency (%)
306
76.9%
- 92
 
23.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14553
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1490
 
10.2%
e 1456
 
10.0%
o 1304
 
9.0%
t 1098
 
7.5%
d 1053
 
7.2%
n 1005
 
6.9%
u 816
 
5.6%
r 674
 
4.6%
c 638
 
4.4%
l 614
 
4.2%
Other values (15) 4405
30.3%

Marital_Status
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
Married
709 
Single
668 
Unknown
129 
Divorced
121 

Length

Max length8
Median length7
Mean length6.6637984
Min length6

Characters and Unicode

Total characters10842
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowMarried
3rd rowMarried
4th rowMarried
5th rowMarried

Common Values

ValueCountFrequency (%)
Married 709
43.6%
Single 668
41.1%
Unknown 129
 
7.9%
Divorced 121
 
7.4%

Length

2023-12-27T07:22:15.785461image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-27T07:22:15.828550image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
married 709
43.6%
single 668
41.1%
unknown 129
 
7.9%
divorced 121
 
7.4%

Most occurring characters

ValueCountFrequency (%)
r 1539
14.2%
i 1498
13.8%
e 1498
13.8%
n 1055
9.7%
d 830
7.7%
M 709
6.5%
a 709
6.5%
l 668
6.2%
g 668
6.2%
S 668
6.2%
Other values (7) 1000
9.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9215
85.0%
Uppercase Letter 1627
 
15.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1539
16.7%
i 1498
16.3%
e 1498
16.3%
n 1055
11.4%
d 830
9.0%
a 709
7.7%
l 668
7.2%
g 668
7.2%
o 250
 
2.7%
k 129
 
1.4%
Other values (3) 371
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
M 709
43.6%
S 668
41.1%
U 129
 
7.9%
D 121
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 10842
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1539
14.2%
i 1498
13.8%
e 1498
13.8%
n 1055
9.7%
d 830
7.7%
M 709
6.5%
a 709
6.5%
l 668
6.2%
g 668
6.2%
S 668
6.2%
Other values (7) 1000
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1539
14.2%
i 1498
13.8%
e 1498
13.8%
n 1055
9.7%
d 830
7.7%
M 709
6.5%
a 709
6.5%
l 668
6.2%
g 668
6.2%
S 668
6.2%
Other values (7) 1000
9.2%

Income_Category
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
Less than $40K
612 
$40K - $60K
271 
$80K - $120K
242 
$60K - $80K
189 
Unknown
187 

Length

Max length14
Median length12
Mean length11.507683
Min length7

Characters and Unicode

Total characters18723
Distinct characters22
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLess than $40K
2nd rowUnknown
3rd rowLess than $40K
4th row$120K +
5th row$60K - $80K

Common Values

ValueCountFrequency (%)
Less than $40K 612
37.6%
$40K - $60K 271
16.7%
$80K - $120K 242
 
14.9%
$60K - $80K 189
 
11.6%
Unknown 187
 
11.5%
$120K + 126
 
7.7%

Length

2023-12-27T07:22:15.876941image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-27T07:22:15.928467image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
40k 883
20.2%
828
18.9%
less 612
14.0%
than 612
14.0%
60k 460
10.5%
80k 431
9.8%
120k 368
8.4%
unknown 187
 
4.3%

Most occurring characters

ValueCountFrequency (%)
2754
14.7%
K 2142
11.4%
0 2142
11.4%
$ 2142
11.4%
s 1224
 
6.5%
n 1173
 
6.3%
4 883
 
4.7%
- 702
 
3.7%
e 612
 
3.3%
L 612
 
3.3%
Other values (12) 4337
23.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5406
28.9%
Decimal Number 4652
24.8%
Uppercase Letter 2941
15.7%
Space Separator 2754
14.7%
Currency Symbol 2142
 
11.4%
Dash Punctuation 702
 
3.7%
Math Symbol 126
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 1224
22.6%
n 1173
21.7%
e 612
11.3%
a 612
11.3%
h 612
11.3%
t 612
11.3%
k 187
 
3.5%
o 187
 
3.5%
w 187
 
3.5%
Decimal Number
ValueCountFrequency (%)
0 2142
46.0%
4 883
19.0%
6 460
 
9.9%
8 431
 
9.3%
1 368
 
7.9%
2 368
 
7.9%
Uppercase Letter
ValueCountFrequency (%)
K 2142
72.8%
L 612
 
20.8%
U 187
 
6.4%
Space Separator
ValueCountFrequency (%)
2754
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 2142
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 702
100.0%
Math Symbol
ValueCountFrequency (%)
+ 126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10376
55.4%
Latin 8347
44.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 2142
25.7%
s 1224
14.7%
n 1173
14.1%
e 612
 
7.3%
L 612
 
7.3%
a 612
 
7.3%
h 612
 
7.3%
t 612
 
7.3%
U 187
 
2.2%
k 187
 
2.2%
Other values (2) 374
 
4.5%
Common
ValueCountFrequency (%)
2754
26.5%
0 2142
20.6%
$ 2142
20.6%
4 883
 
8.5%
- 702
 
6.8%
6 460
 
4.4%
8 431
 
4.2%
1 368
 
3.5%
2 368
 
3.5%
+ 126
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18723
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2754
14.7%
K 2142
11.4%
0 2142
11.4%
$ 2142
11.4%
s 1224
 
6.5%
n 1173
 
6.3%
4 883
 
4.7%
- 702
 
3.7%
e 612
 
3.3%
L 612
 
3.3%
Other values (12) 4337
23.2%

Card_Category
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
Blue
1519 
Silver
 
82
Gold
 
21
Platinum
 
5

Length

Max length8
Median length4
Mean length4.1130916
Min length4

Characters and Unicode

Total characters6692
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlue
2nd rowBlue
3rd rowBlue
4th rowBlue
5th rowSilver

Common Values

ValueCountFrequency (%)
Blue 1519
93.4%
Silver 82
 
5.0%
Gold 21
 
1.3%
Platinum 5
 
0.3%

Length

2023-12-27T07:22:15.989061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-27T07:22:16.032988image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
blue 1519
93.4%
silver 82
 
5.0%
gold 21
 
1.3%
platinum 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
l 1627
24.3%
e 1601
23.9%
u 1524
22.8%
B 1519
22.7%
i 87
 
1.3%
S 82
 
1.2%
v 82
 
1.2%
r 82
 
1.2%
G 21
 
0.3%
o 21
 
0.3%
Other values (6) 46
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5065
75.7%
Uppercase Letter 1627
 
24.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 1627
32.1%
e 1601
31.6%
u 1524
30.1%
i 87
 
1.7%
v 82
 
1.6%
r 82
 
1.6%
o 21
 
0.4%
d 21
 
0.4%
a 5
 
0.1%
t 5
 
0.1%
Other values (2) 10
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
B 1519
93.4%
S 82
 
5.0%
G 21
 
1.3%
P 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 6692
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 1627
24.3%
e 1601
23.9%
u 1524
22.8%
B 1519
22.7%
i 87
 
1.3%
S 82
 
1.2%
v 82
 
1.2%
r 82
 
1.2%
G 21
 
0.3%
o 21
 
0.3%
Other values (6) 46
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6692
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 1627
24.3%
e 1601
23.9%
u 1524
22.8%
B 1519
22.7%
i 87
 
1.3%
S 82
 
1.2%
v 82
 
1.2%
r 82
 
1.2%
G 21
 
0.3%
o 21
 
0.3%
Other values (6) 46
 
0.7%

Months_on_book
Real number (ℝ)

Distinct44
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.178242
Minimum13
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-12-27T07:22:16.077193image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile22
Q132
median36
Q340
95-th percentile50
Maximum56
Range43
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.7965485
Coefficient of variation (CV)0.2155038
Kurtosis0.44871053
Mean36.178242
Median Absolute Deviation (MAD)4
Skewness-0.11867294
Sum58862
Variance60.786168
MonotonicityNot monotonic
2023-12-27T07:22:16.123635image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
36 430
26.4%
39 64
 
3.9%
37 62
 
3.8%
30 58
 
3.6%
34 57
 
3.5%
38 57
 
3.5%
41 51
 
3.1%
33 48
 
3.0%
40 45
 
2.8%
35 45
 
2.8%
Other values (34) 710
43.6%
ValueCountFrequency (%)
13 7
 
0.4%
14 1
 
0.1%
15 9
0.6%
16 3
 
0.2%
17 4
 
0.2%
18 13
0.8%
19 6
 
0.4%
20 13
0.8%
21 10
0.6%
22 20
1.2%
ValueCountFrequency (%)
56 17
1.0%
55 4
 
0.2%
54 6
 
0.4%
53 7
 
0.4%
52 12
0.7%
51 16
1.0%
50 25
1.5%
49 24
1.5%
48 27
1.7%
47 24
1.5%

Total_Relationship_Count
Real number (ℝ)

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2796558
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-12-27T07:22:16.161687image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5777817
Coefficient of variation (CV)0.4810815
Kurtosis-1.0128313
Mean3.2796558
Median Absolute Deviation (MAD)1
Skewness0.26517933
Sum5336
Variance2.4893952
MonotonicityNot monotonic
2023-12-27T07:22:16.199598image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 400
24.6%
2 346
21.3%
1 233
14.3%
5 227
14.0%
4 225
13.8%
6 196
12.0%
ValueCountFrequency (%)
1 233
14.3%
2 346
21.3%
3 400
24.6%
4 225
13.8%
5 227
14.0%
6 196
12.0%
ValueCountFrequency (%)
6 196
12.0%
5 227
14.0%
4 225
13.8%
3 400
24.6%
2 346
21.3%
1 233
14.3%

Months_Inactive_12_mon
Real number (ℝ)

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6933006
Minimum0
Maximum6
Zeros15
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-12-27T07:22:16.234081image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.89962305
Coefficient of variation (CV)0.33402252
Kurtosis1.9816551
Mean2.6933006
Median Absolute Deviation (MAD)0
Skewness0.37782838
Sum4382
Variance0.80932163
MonotonicityNot monotonic
2023-12-27T07:22:16.270172image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 826
50.8%
2 505
31.0%
4 130
 
8.0%
1 100
 
6.1%
5 32
 
2.0%
6 19
 
1.2%
0 15
 
0.9%
ValueCountFrequency (%)
0 15
 
0.9%
1 100
 
6.1%
2 505
31.0%
3 826
50.8%
4 130
 
8.0%
5 32
 
2.0%
6 19
 
1.2%
ValueCountFrequency (%)
6 19
 
1.2%
5 32
 
2.0%
4 130
 
8.0%
3 826
50.8%
2 505
31.0%
1 100
 
6.1%
0 15
 
0.9%

Contacts_Count_12_mon
Real number (ℝ)

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9723417
Minimum0
Maximum6
Zeros7
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-12-27T07:22:16.427509image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0905372
Coefficient of variation (CV)0.36689497
Kurtosis0.67237201
Mean2.9723417
Median Absolute Deviation (MAD)1
Skewness0.45079735
Sum4836
Variance1.1892715
MonotonicityNot monotonic
2023-12-27T07:22:16.467706image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 681
41.9%
2 403
24.8%
4 315
19.4%
1 108
 
6.6%
5 59
 
3.6%
6 54
 
3.3%
0 7
 
0.4%
ValueCountFrequency (%)
0 7
 
0.4%
1 108
 
6.6%
2 403
24.8%
3 681
41.9%
4 315
19.4%
5 59
 
3.6%
6 54
 
3.3%
ValueCountFrequency (%)
6 54
 
3.3%
5 59
 
3.6%
4 315
19.4%
3 681
41.9%
2 403
24.8%
1 108
 
6.6%
0 7
 
0.4%

Credit_Limit
Real number (ℝ)

Distinct1306
Distinct (%)80.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8136.0395
Minimum1438.3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-12-27T07:22:16.515424image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1438.3
5-th percentile1438.3
Q12114
median4178
Q39933.5
95-th percentile34516
Maximum34516
Range33077.7
Interquartile range (IQR)7819.5

Descriptive statistics

Standard deviation9095.3341
Coefficient of variation (CV)1.1179068
Kurtosis2.2943412
Mean8136.0395
Median Absolute Deviation (MAD)2513
Skewness1.8044276
Sum13237336
Variance82725102
MonotonicityNot monotonic
2023-12-27T07:22:16.567611image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1438.3 124
 
7.6%
34516 89
 
5.5%
9959 5
 
0.3%
7970 3
 
0.2%
1606 3
 
0.2%
15987 3
 
0.2%
23981 3
 
0.2%
1666 3
 
0.2%
1931 3
 
0.2%
1695 3
 
0.2%
Other values (1296) 1388
85.3%
ValueCountFrequency (%)
1438.3 124
7.6%
1439 1
 
0.1%
1440 1
 
0.1%
1442 1
 
0.1%
1443 1
 
0.1%
1451 1
 
0.1%
1452 1
 
0.1%
1456 1
 
0.1%
1457 2
 
0.1%
1460 1
 
0.1%
ValueCountFrequency (%)
34516 89
5.5%
34162 1
 
0.1%
34140 1
 
0.1%
33870 1
 
0.1%
33384 1
 
0.1%
33180 1
 
0.1%
33004 1
 
0.1%
32975 1
 
0.1%
32938 1
 
0.1%
32838 1
 
0.1%

Total_Revolving_Bal
Real number (ℝ)

ZEROS 

Distinct514
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean672.82299
Minimum0
Maximum2517
Zeros893
Zeros (%)54.9%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-12-27T07:22:16.619750image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31303.5
95-th percentile2517
Maximum2517
Range2517
Interquartile range (IQR)1303.5

Descriptive statistics

Standard deviation921.38558
Coefficient of variation (CV)1.3694324
Kurtosis-0.53447407
Mean672.82299
Median Absolute Deviation (MAD)0
Skewness1.0240548
Sum1094683
Variance848951.39
MonotonicityNot monotonic
2023-12-27T07:22:16.686527image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 893
54.9%
2517 158
 
9.7%
710 3
 
0.2%
458 3
 
0.2%
1381 3
 
0.2%
321 3
 
0.2%
1474 2
 
0.1%
230 2
 
0.1%
531 2
 
0.1%
725 2
 
0.1%
Other values (504) 556
34.2%
ValueCountFrequency (%)
0 893
54.9%
132 1
 
0.1%
134 1
 
0.1%
145 1
 
0.1%
154 1
 
0.1%
157 1
 
0.1%
159 2
 
0.1%
168 2
 
0.1%
170 1
 
0.1%
186 1
 
0.1%
ValueCountFrequency (%)
2517 158
9.7%
2514 1
 
0.1%
2513 1
 
0.1%
2507 1
 
0.1%
2505 1
 
0.1%
2498 1
 
0.1%
2488 1
 
0.1%
2480 1
 
0.1%
2477 1
 
0.1%
2466 1
 
0.1%

Avg_Open_To_Buy
Real number (ℝ)

Distinct1392
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7463.2165
Minimum3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-12-27T07:22:16.740154image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile442
Q11587
median3488
Q39257.5
95-th percentile32287.7
Maximum34516
Range34513
Interquartile range (IQR)7670.5

Descriptive statistics

Standard deviation9109.2081
Coefficient of variation (CV)1.2205472
Kurtosis2.2807365
Mean7463.2165
Median Absolute Deviation (MAD)2286
Skewness1.7994396
Sum12142653
Variance82977673
MonotonicityNot monotonic
2023-12-27T07:22:16.796502image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1438.3 96
 
5.9%
34516 39
 
2.4%
31999 10
 
0.6%
1818 3
 
0.2%
1695 3
 
0.2%
9959 3
 
0.2%
1568 3
 
0.2%
2089 3
 
0.2%
32669 2
 
0.1%
3919 2
 
0.1%
Other values (1382) 1463
89.9%
ValueCountFrequency (%)
3 1
0.1%
10 1
0.1%
14 2
0.1%
24 1
0.1%
28 1
0.1%
36 1
0.1%
48 1
0.1%
58 1
0.1%
59 1
0.1%
79 1
0.1%
ValueCountFrequency (%)
34516 39
2.4%
34362 1
 
0.1%
34302 1
 
0.1%
34300 1
 
0.1%
34297 1
 
0.1%
34286 1
 
0.1%
34238 1
 
0.1%
34227 1
 
0.1%
34140 1
 
0.1%
34119 1
 
0.1%

Total_Amt_Chng_Q4_Q1
Real number (ℝ)

Distinct690
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69427658
Minimum0
Maximum1.492
Zeros5
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-12-27T07:22:16.847868image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3483
Q10.5445
median0.701
Q30.856
95-th percentile1.032
Maximum1.492
Range1.492
Interquartile range (IQR)0.3115

Descriptive statistics

Standard deviation0.21492433
Coefficient of variation (CV)0.30956586
Kurtosis-0.092213414
Mean0.69427658
Median Absolute Deviation (MAD)0.156
Skewness-0.21521738
Sum1129.588
Variance0.046192466
MonotonicityNot monotonic
2023-12-27T07:22:16.901399image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.602 8
 
0.5%
0.735 7
 
0.4%
0.717 7
 
0.4%
0.654 7
 
0.4%
0.703 7
 
0.4%
0.713 7
 
0.4%
0.544 6
 
0.4%
0.69 6
 
0.4%
0.89 6
 
0.4%
0.639 6
 
0.4%
Other values (680) 1560
95.9%
ValueCountFrequency (%)
0 5
0.3%
0.01 1
 
0.1%
0.018 1
 
0.1%
0.046 1
 
0.1%
0.061 2
 
0.1%
0.072 1
 
0.1%
0.101 1
 
0.1%
0.12 1
 
0.1%
0.153 1
 
0.1%
0.163 1
 
0.1%
ValueCountFrequency (%)
1.492 1
0.1%
1.411 1
0.1%
1.336 1
0.1%
1.23 1
0.1%
1.214 1
0.1%
1.203 1
0.1%
1.166 1
0.1%
1.144 1
0.1%
1.129 1
0.1%
1.087 1
0.1%

Total_Trans_Amt
Real number (ℝ)

Distinct1266
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3095.0258
Minimum510
Maximum10583
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-12-27T07:22:16.954497image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum510
5-th percentile837
Q11903.5
median2329
Q32772
95-th percentile8659.7
Maximum10583
Range10073
Interquartile range (IQR)868.5

Descriptive statistics

Standard deviation2308.2276
Coefficient of variation (CV)0.74578623
Kurtosis1.6539713
Mean3095.0258
Median Absolute Deviation (MAD)431
Skewness1.6853362
Sum5035607
Variance5327914.8
MonotonicityNot monotonic
2023-12-27T07:22:17.004518image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2216 5
 
0.3%
2108 5
 
0.3%
2312 5
 
0.3%
2400 4
 
0.2%
2527 4
 
0.2%
2264 4
 
0.2%
2281 4
 
0.2%
2164 4
 
0.2%
2306 4
 
0.2%
2196 4
 
0.2%
Other values (1256) 1584
97.4%
ValueCountFrequency (%)
510 1
0.1%
530 1
0.1%
563 1
0.1%
569 1
0.1%
594 1
0.1%
596 1
0.1%
597 1
0.1%
602 1
0.1%
615 1
0.1%
643 1
0.1%
ValueCountFrequency (%)
10583 1
0.1%
10468 1
0.1%
10310 1
0.1%
10294 1
0.1%
10291 1
0.1%
10219 1
0.1%
10211 1
0.1%
10201 1
0.1%
10170 1
0.1%
10156 1
0.1%

Total_Trans_Ct
Real number (ℝ)

Distinct82
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.93362
Minimum10
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-12-27T07:22:17.053679image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile21
Q137
median43
Q351
95-th percentile74
Maximum94
Range84
Interquartile range (IQR)14

Descriptive statistics

Standard deviation14.568429
Coefficient of variation (CV)0.32422114
Kurtosis0.57080521
Mean44.93362
Median Absolute Deviation (MAD)7
Skewness0.4859448
Sum73107
Variance212.23913
MonotonicityNot monotonic
2023-12-27T07:22:17.102412image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 85
 
5.2%
42 75
 
4.6%
44 69
 
4.2%
40 69
 
4.2%
41 67
 
4.1%
45 61
 
3.7%
39 58
 
3.6%
38 56
 
3.4%
46 54
 
3.3%
47 48
 
3.0%
Other values (72) 985
60.5%
ValueCountFrequency (%)
10 4
 
0.2%
11 1
 
0.1%
12 4
 
0.2%
13 3
 
0.2%
14 8
0.5%
15 12
0.7%
16 8
0.5%
17 10
0.6%
18 15
0.9%
19 7
0.4%
ValueCountFrequency (%)
94 1
 
0.1%
91 1
 
0.1%
90 3
0.2%
89 2
 
0.1%
87 5
0.3%
86 1
 
0.1%
85 4
0.2%
84 4
0.2%
83 1
 
0.1%
82 4
0.2%

Total_Ct_Chng_Q4_Q1
Real number (ℝ)

Distinct428
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55438599
Minimum0
Maximum2.5
Zeros7
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-12-27T07:22:17.152113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.233
Q10.4
median0.531
Q30.692
95-th percentile0.9318
Maximum2.5
Range2.5
Interquartile range (IQR)0.292

Descriptive statistics

Standard deviation0.22685372
Coefficient of variation (CV)0.40919815
Kurtosis5.3069084
Mean0.55438599
Median Absolute Deviation (MAD)0.138
Skewness1.0503559
Sum901.986
Variance0.05146261
MonotonicityNot monotonic
2023-12-27T07:22:17.205851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 58
 
3.6%
0.6 25
 
1.5%
0.4 25
 
1.5%
0.429 21
 
1.3%
0.75 19
 
1.2%
0.333 18
 
1.1%
1 17
 
1.0%
0.667 16
 
1.0%
0.375 14
 
0.9%
0.467 14
 
0.9%
Other values (418) 1400
86.0%
ValueCountFrequency (%)
0 7
0.4%
0.029 1
 
0.1%
0.038 1
 
0.1%
0.053 1
 
0.1%
0.059 2
 
0.1%
0.074 1
 
0.1%
0.077 3
0.2%
0.091 3
0.2%
0.097 1
 
0.1%
0.103 1
 
0.1%
ValueCountFrequency (%)
2.5 1
0.1%
2.222 1
0.1%
1.684 1
0.1%
1.5 1
0.1%
1.444 1
0.1%
1.294 1
0.1%
1.273 1
0.1%
1.25 2
0.1%
1.217 1
0.1%
1.211 1
0.1%

Avg_Utilization_Ratio
Real number (ℝ)

ZEROS 

Distinct490
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16247511
Minimum0
Maximum0.999
Zeros893
Zeros (%)54.9%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-12-27T07:22:17.259263image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.231
95-th percentile0.8154
Maximum0.999
Range0.999
Interquartile range (IQR)0.231

Descriptive statistics

Standard deviation0.26445762
Coefficient of variation (CV)1.6276809
Kurtosis1.4231341
Mean0.16247511
Median Absolute Deviation (MAD)0
Skewness1.6301497
Sum264.347
Variance0.069937834
MonotonicityNot monotonic
2023-12-27T07:22:17.311598image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 893
54.9%
0.073 11
 
0.7%
0.318 5
 
0.3%
0.112 5
 
0.3%
0.075 4
 
0.2%
0.012 4
 
0.2%
0.031 4
 
0.2%
0.039 4
 
0.2%
0.094 4
 
0.2%
0.051 4
 
0.2%
Other values (480) 689
42.3%
ValueCountFrequency (%)
0 893
54.9%
0.004 1
 
0.1%
0.005 1
 
0.1%
0.006 3
 
0.2%
0.007 1
 
0.1%
0.008 2
 
0.1%
0.009 1
 
0.1%
0.01 1
 
0.1%
0.011 1
 
0.1%
0.012 4
 
0.2%
ValueCountFrequency (%)
0.999 1
0.1%
0.995 1
0.1%
0.992 1
0.1%
0.99 1
0.1%
0.987 1
0.1%
0.985 1
0.1%
0.983 1
0.1%
0.977 1
0.1%
0.976 1
0.1%
0.972 1
0.1%
Distinct420
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9949086
Minimum0.94591
Maximum0.99958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-12-27T07:22:17.362659image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.94591
5-th percentile0.98739
Q10.9943
median0.99631
Q30.99717
95-th percentile0.998897
Maximum0.99958
Range0.05367
Interquartile range (IQR)0.00287

Descriptive statistics

Standard deviation0.0043032754
Coefficient of variation (CV)0.0043252972
Kurtosis24.352519
Mean0.9949086
Median Absolute Deviation (MAD)0.00164
Skewness-3.7043357
Sum1618.7163
Variance1.8518179 × 10-5
MonotonicityNot monotonic
2023-12-27T07:22:17.416216image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.99452 29
 
1.8%
0.99671 27
 
1.7%
0.99499 26
 
1.6%
0.99447 26
 
1.6%
0.99639 24
 
1.5%
0.99683 22
 
1.4%
0.99686 22
 
1.4%
0.99808 20
 
1.2%
0.99661 20
 
1.2%
0.99673 19
 
1.2%
Other values (410) 1392
85.6%
ValueCountFrequency (%)
0.94591 1
0.1%
0.95195 1
0.1%
0.9661 1
0.1%
0.9669 1
0.1%
0.96806 1
0.1%
0.96902 1
0.1%
0.97066 1
0.1%
0.97132 1
0.1%
0.97155 1
0.1%
0.97166 1
0.1%
ValueCountFrequency (%)
0.99958 3
0.2%
0.99954 1
 
0.1%
0.99945 3
0.2%
0.99944 4
0.2%
0.99943 1
 
0.1%
0.99942 2
 
0.1%
0.99939 6
0.4%
0.99938 5
0.3%
0.99937 2
 
0.1%
0.99936 1
 
0.1%

Attrition_Num
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
1
1627 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1627
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1627
100.0%

Length

2023-12-27T07:22:17.462506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-27T07:22:17.497649image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1627
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1627
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1627
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1627
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1627
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1627
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1627
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1627
100.0%

Interactions

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2023-12-27T07:22:09.251125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:09.868359image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:10.604673image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:11.287501image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:11.983296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:12.620110image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:13.220244image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2023-12-27T07:22:14.692434image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:04.947964image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:05.531254image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:06.109007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:06.738590image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:07.354088image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2023-12-27T07:22:14.741104image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:04.987760image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:05.568680image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:06.147028image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:06.781064image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:07.396783image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:08.114483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:08.720627image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:09.328933image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:09.943991image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:10.688807image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:11.377638image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:12.063649image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:12.697538image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:13.304138image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:14.078813image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:14.788711image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:05.026792image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:05.608239image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:06.188928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:06.820550image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:07.437441image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:08.155927image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:08.762375image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:09.369217image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:09.985257image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:10.730403image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:11.423001image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:12.102957image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2023-12-27T07:22:13.346318image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2023-12-27T07:22:05.643442image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2023-12-27T07:22:14.153720image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2023-12-27T07:22:10.097913image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:10.851533image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:11.549197image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2023-12-27T07:22:14.225021image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2023-12-27T07:22:05.170282image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2023-12-27T07:22:06.345423image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2023-12-27T07:22:07.586141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2023-12-27T07:22:09.528220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:10.135332image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:10.892032image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:11.589146image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:12.255176image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:12.879102image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:13.495242image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-27T07:22:14.262284image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2023-12-27T07:22:15.042671image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-27T07:22:15.163206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CLIENTNUMAttrition_FlagCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioNaive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1Attrition_Num
21708508758Attrited Customer62F0GraduateMarriedLess than $40KBlue492331438.301438.31.047692160.6000.0000.996161
39708300483Attrited Customer66F0DoctorateMarriedUnknownBlue565437882.06057277.01.052704160.1430.0770.997801
51779471883Attrited Customer54F1GraduateMarriedLess than $40KBlue402311438.3808630.30.997705190.9000.5620.990281
54714374133Attrited Customer56M2GraduateMarried$120K +Blue3613315769.0015769.01.041602150.3640.0000.996711
61712030833Attrited Customer48M2GraduateMarried$60K - $80KSilver3524434516.0034516.00.763691150.5000.0000.998231
82711013983Attrited Customer55F4UnknownMarried$40K - $60KBlue452432158.002158.00.585615120.7140.0000.997631
99711887583Attrited Customer47M2UnknownMarried$80K - $120KBlue372335449.016283821.00.696836180.3850.2990.997001
127720201033Attrited Customer53M2GraduateMarried$80K - $120KBlue4133211669.022279442.00.622720230.3530.1910.994471
140789322833Attrited Customer48F5High SchoolMarriedLess than $40KBlue381338025.008025.00.654673180.8000.0000.996721
144767712558Attrited Customer59M1CollegeSingle$60K - $80KBlue5323314979.0014979.00.710530101.0000.0000.996391
CLIENTNUMAttrition_FlagCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioNaive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1Attrition_Num
10107713924283Attrited Customer61M0GraduateSingle$60K - $80KBlue5421411859.0164410215.00.8668930790.8370.1390.992681
10108714471183Attrited Customer47M4GraduateDivorced$80K - $120KBlue3943417504.047617028.00.89210468660.7370.0270.998161
10112708564858Attrited Customer33M2CollegeMarried$120K +Gold2021434516.0034516.01.0049338730.6220.0000.994381
10113713733633Attrited Customer27M0High SchoolDivorced$60K - $80KBlue3623213303.0251710786.00.92910219850.8090.1890.993381
10118713755458Attrited Customer50M1UnknownUnknown$80K - $120KBlue366349959.09529007.00.82510310631.1000.0960.998131
10119716893683Attrited Customer55F3UneducatedSingleUnknownBlue4743314657.0251712140.00.1666009530.5140.1720.996911
10123710638233Attrited Customer41M2UnknownDivorced$40K - $60KBlue254234277.021862091.00.8048764690.6830.5110.995271
10124716506083Attrited Customer44F1High SchoolMarriedLess than $40KBlue365345409.005409.00.81910291600.8180.0000.997881
10125717406983Attrited Customer30M2GraduateUnknown$40K - $60KBlue364335281.005281.00.5358395620.7220.0000.996711
10126714337233Attrited Customer43F2GraduateMarriedLess than $40KSilver2562410388.019618427.00.70310294610.6490.1890.996621